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Volumetric barrier decomposition algorithms for stochastic quadratic second-order cone programming

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  • Alzalg, Baha

Abstract

Ariyawansa and Zhu (2011) have derived volumetric barrier decomposition algorithms for solving two-stage stochastic semidefinite programs and proved polynomial complexity of certain members of the algorithms. In this paper, we utilize their work to derive volumetric barrier decomposition algorithms for solving two-stage stochastic convex quadratic second-order cone programming, and establish polynomial complexity of certain members of the proposed algorithms.

Suggested Citation

  • Alzalg, Baha, 2015. "Volumetric barrier decomposition algorithms for stochastic quadratic second-order cone programming," Applied Mathematics and Computation, Elsevier, vol. 265(C), pages 494-508.
    • Handle: RePEc:eee:apmaco:v:265:y:2015:i:c:p:494-508
    DOI: 10.1016/j.amc.2015.05.014
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    References listed on IDEAS

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    1. F. Maggioni & F. A. Potra & M. I. Bertocchi & E. Allevi, 2009. "Stochastic Second-Order Cone Programming in Mobile Ad Hoc Networks," Journal of Optimization Theory and Applications, Springer, vol. 143(2), pages 309-328, November.
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    Cited by:

    1. Baha Alzalg & Khaled Badarneh & Ayat Ababneh, 2019. "An Infeasible Interior-Point Algorithm for Stochastic Second-Order Cone Optimization," Journal of Optimization Theory and Applications, Springer, vol. 181(1), pages 324-346, April.
    2. Baha Alzalg & Asma Gafour, 2023. "Convergence of a Weighted Barrier Algorithm for Stochastic Convex Quadratic Semidefinite Optimization," Journal of Optimization Theory and Applications, Springer, vol. 196(2), pages 490-515, February.
    3. Baha Alzalg, 2019. "A primal-dual interior-point method based on various selections of displacement step for symmetric optimization," Computational Optimization and Applications, Springer, vol. 72(2), pages 363-390, March.

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